Traditionally, radiology is mainly used to diagnose and evaluate the treatment effect of breast tumors by describing the morphological characteristics of breast lesions. However, this kind of imaging qualitative analysis method is based on radiologists’ subjective assessment. In addition, the quantitative analysis method based on conventional medical images is usually simplistic and unable to establish an accurate diagnostic threshold to reflect the microstructure characteristics of breast tumors. Breast imaging modalities (i.e. mammography, ultrasonography and MRI) are common and significant imaging tools for breast tumor screening, and quantitative imaging which based on these modalities provides objective analysis for breast tumor diagnosis. Artificial intelligence, mainly including machine learning algorithms, deep learning, and radiomics, serves as a promising quantitative analysis method rising in recent years, extracting high-throughput information from medical images and comprehensive application of clinicopathologic data. Quantitative imaging together with artificial intelligence plays a key role in the preoperative precise diagnosis and individualized treatment decision-making of patients with breast tumors.
This research topic aims at exploring novel imaging quantitative analysis methods or artificial intelligence technology, applying the traditional imaging quantitative or artificial intelligence technology to clinical problems, to provide new analysis tools and perspectives for preoperative diagnosis of breast tumors.
We welcome Original Research, Review, and State-of-the-art Brief Communications, but not limited to the following subjects:
• Discovery of novel quantitative imaging tool for early screening and diagnosis of breast tumor.
• Exploration and optimization of quantitative imaging technology to make a better diagnosis, classification or staging for breast tumors.
• Construct medical image segmentation, detection, classification, diagnosis methods of breast tumor using artificial intelligence technology (including but not limited to deep learning, machine learning, and radiomics).
• Develop a new integration strategy of diagnosis and treatment associated with breast tumors by using a quantitative imaging approach or artificial intelligence architecture.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Traditionally, radiology is mainly used to diagnose and evaluate the treatment effect of breast tumors by describing the morphological characteristics of breast lesions. However, this kind of imaging qualitative analysis method is based on radiologists’ subjective assessment. In addition, the quantitative analysis method based on conventional medical images is usually simplistic and unable to establish an accurate diagnostic threshold to reflect the microstructure characteristics of breast tumors. Breast imaging modalities (i.e. mammography, ultrasonography and MRI) are common and significant imaging tools for breast tumor screening, and quantitative imaging which based on these modalities provides objective analysis for breast tumor diagnosis. Artificial intelligence, mainly including machine learning algorithms, deep learning, and radiomics, serves as a promising quantitative analysis method rising in recent years, extracting high-throughput information from medical images and comprehensive application of clinicopathologic data. Quantitative imaging together with artificial intelligence plays a key role in the preoperative precise diagnosis and individualized treatment decision-making of patients with breast tumors.
This research topic aims at exploring novel imaging quantitative analysis methods or artificial intelligence technology, applying the traditional imaging quantitative or artificial intelligence technology to clinical problems, to provide new analysis tools and perspectives for preoperative diagnosis of breast tumors.
We welcome Original Research, Review, and State-of-the-art Brief Communications, but not limited to the following subjects:
• Discovery of novel quantitative imaging tool for early screening and diagnosis of breast tumor.
• Exploration and optimization of quantitative imaging technology to make a better diagnosis, classification or staging for breast tumors.
• Construct medical image segmentation, detection, classification, diagnosis methods of breast tumor using artificial intelligence technology (including but not limited to deep learning, machine learning, and radiomics).
• Develop a new integration strategy of diagnosis and treatment associated with breast tumors by using a quantitative imaging approach or artificial intelligence architecture.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
